1. Field of the Invention
This invention relates to the field of video monitoring and analysis.
2. Description of the Related Art
In response to heightened security demand, more and more video surveillance systems are deployed. However, the number of trained personnel for watching the systems are too limited to monitor all of them effectively, let alone to analyze the vast amount of data collected from the video surveillance systems offline. Thus, automatic mining and detection of abnormal events is necessary, and it can be generally formulated as a classification of time series. A supervised approach to event detection and recognition learns a model of reference event sequences from training samples and then devises a matching criterion such that they can accommodate variations within one event class while discriminating between different event classes. Dynamic Time Warping (DTW), a matching technique widely used for speech recognition, has been used in recognizing human motion pattern. Finite-state machine (FSM) is also used for modeling vehicle motion from aerial images. One common assumption of those supervised approaches is that the events of interest are known in advance and can be modeled accordingly. To detect unusual events which are not seen before, unsupervised and semi-supervised approaches are necessary. The performance of such approaches depends on the number of iterations, which has to be manually set case by case, causing high occurrences of false alarm.
Abnormal events that are of interests to surveillance application can be characterized by its rarity (seldom occurs) and unexpectedness (unseen before). The rarity of abnormal events limits the number of training data for such events. Although each suspicious (abnormal) activity before a major attack may not be enough to trigger an alarm if considered individually, it will be much more obvious that some abnormal event is going to happen if the evidences from distant sites are combined. Meanwhile, the distinctions between two abnormal events are usually as large as that between an abnormal event and a normal event, making it hard to train a model for a general abnormal event. On the other hand, there exist abundant training data for normal events, and those normal events tend to cluster in a few regions.